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An empirical study of the effect of learning styles on the faults found during the software requirements inspection
  • Anurag Goswami, G. Walia
  • Engineering, Computer Science
  • IEEE 24th International Symposium on Software…
  • 1 November 2013
The initial results show that the teams composed of inspectors with different LS preferences are more effective and efficient than the teams of inspectors who had similar LS's. Expand
Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach
This work aims at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults during the fault consolidation step of requirements inspections. Expand
Using Learning Styles to Staff and Improve Software Inspection Team Performance
The results showed that the inspection teams formed with inspectors of diverse LSs outperformed teams with similar LSs of inspectors, and can help software managers better staff inspectors, enabling cost savings, and improving quality. Expand
Using Eye Tracking to Investigate Reading Patterns and Learning Styles of Software Requirement Inspectors to Enhance Inspection Team Outcome
Analysis of reading trends of effective and efficient inspectors using eye movement and LS data of individual inspectors and virtual inspection teams shows inspectors who detect more faults during inspection, focus significantly more at the fault region to find and report faults as opposed to comprehending requirements information. Expand
Using Learning Styles of Software Professionals to Improve Their Inspection Team Performance
Results showed inspection ability does not depend on educational background and technical knowledge, and LS’s can aid software managers to create high performance inspection team(s) and manage software quality. Expand
Teaching Software Requirements Inspections to Software Engineering Students through Practical Training and Reflection
Teaching Software Requirements Inspections to Software Engineering Students through Practical Training and Reflection and teaching software quality through practical training and reflection. Expand
Validation of Inspection Reviews over Variable Features Set Threshold
The results show that the most stable and promising validation results for F-measure and G-mean are obtained when a model over inspection and movies reviews are trained using feature set threshold value 65% and 45% respectively. Expand
An Empirical Investigation to Overcome Class-Imbalance in Inspection Reviews
This research uses ensemble methods to develop classification confidence of inspection reviews and assigns them to appropriate priority class, and shows that movies trained model performed better than inspection trained and restricted any possible fault-slippage. Expand
Using Supervised Learning to Guide the Selection of Software Inspectors in Industry
This study analyzes the reading patterns (RPs) of inspectors recorded by eye-tracking equipment and evaluates their abilities to find various fault-types, showing that the approach could guide the inspector selection with an accuracy ranging between 79.3% and 94% forVarious fault- types. Expand